How can you join multiple streams or tables together using a single expression in ksqlDB?
Multi-way joins:
CREATE STREAM orders_enriched AS
SELECT customers.customerid AS customerid, customers.customername AS customername,
orders.orderid, orders.purchasedate,
items.itemid, items.itemname
FROM orders
LEFT JOIN customers on orders.customerid = customers.customerid
LEFT JOIN items on orders.itemid = items.itemid;
This tutorial installs Confluent Platform using Docker. Before proceeding:
• Install Docker Desktop (version 4.0.0
or later) or Docker Engine (version 19.03.0
or later) if you don’t already have it
• Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.
• Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd
, via systemctl
• Verify that Docker is set up properly by ensuring no errors are output when you run docker info
and docker compose version
on the command line
To get started, make a new directory anywhere you’d like for this project:
mkdir multi-joins && cd multi-joins
Then make the following directories to set up its structure:
mkdir src test
Next, create the following docker-compose.yml
file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):
version: '2'
services:
broker:
image: confluentinc/cp-kafka:7.4.1
hostname: broker
container_name: broker
ports:
- 29092:29092
environment:
KAFKA_BROKER_ID: 1
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_PROCESS_ROLES: broker,controller
KAFKA_NODE_ID: 1
KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
schema-registry:
image: confluentinc/cp-schema-registry:7.3.0
hostname: schema-registry
container_name: schema-registry
depends_on:
- broker
ports:
- 8081:8081
environment:
SCHEMA_REGISTRY_HOST_NAME: schema-registry
SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
ksqldb-server:
image: confluentinc/ksqldb-server:0.28.2
hostname: ksqldb-server
container_name: ksqldb-server
depends_on:
- broker
- schema-registry
ports:
- 8088:8088
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
KSQL_LOG4J_OPTS: -Dlog4j.configuration=file:/etc/ksqldb/log4j.properties
KSQL_BOOTSTRAP_SERVERS: broker:9092
KSQL_HOST_NAME: ksqldb-server
KSQL_LISTENERS: http://0.0.0.0:8088
KSQL_CACHE_MAX_BYTES_BUFFERING: 0
KSQL_KSQL_SCHEMA_REGISTRY_URL: http://schema-registry:8081
ksqldb-cli:
image: confluentinc/ksqldb-cli:0.28.2
container_name: ksqldb-cli
depends_on:
- broker
- ksqldb-server
entrypoint: /bin/sh
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
tty: true
volumes:
- ./src:/opt/app/src
- ./test:/opt/app/test
And launch it by running:
docker compose up -d
To create our application, we’ll first model some input data to mimic an online store. We will then use the ksqlDB multi-join feature to create a Stream of orders enriched with data from the inputs.
To begin developing interactively, open up the ksqlDB CLI:
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088
First let’s create an input Table of customer data which will hold data in JSON format.
CREATE TABLE customers (customerid STRING PRIMARY KEY, customername STRING)
WITH (KAFKA_TOPIC='customers',
VALUE_FORMAT='json',
PARTITIONS=1);
Similarly, we create a second table containing items available in our online store:
CREATE TABLE items (itemid STRING PRIMARY KEY, itemname STRING)
WITH (KAFKA_TOPIC='items',
VALUE_FORMAT='json',
PARTITIONS=1);
Next we create a stream containing orders submitted to our online store, also formatted in JSON.
CREATE STREAM orders (orderid STRING KEY, customerid STRING, itemid STRING, purchasedate STRING)
WITH (KAFKA_TOPIC='orders',
VALUE_FORMAT='json',
PARTITIONS=1);
Now we will populate our inputs with some sample data.
First some customer data:
INSERT INTO customers VALUES ('1', 'Adrian Garcia');
INSERT INTO customers VALUES ('2', 'Robert Miller');
INSERT INTO customers VALUES ('3', 'Brian Smith');
And some items available in our store:
INSERT INTO items VALUES ('101', 'Television 60-in');
INSERT INTO items VALUES ('102', 'Laptop 15-in');
INSERT INTO items VALUES ('103', 'Speakers');
Then we insert some orders. Each order contains a unique order id, a customer id, an item id, and a purchase date:
INSERT INTO orders VALUES ('abc123', '1', '101', '2020-05-01');
INSERT INTO orders VALUES ('abc345', '1', '102', '2020-05-01');
INSERT INTO orders VALUES ('abc678', '2', '101', '2020-05-01');
INSERT INTO orders VALUES ('abc987', '3', '101', '2020-05-03');
INSERT INTO orders VALUES ('xyz123', '2', '103', '2020-05-03');
INSERT INTO orders VALUES ('xyz987', '2', '102', '2020-05-05');
Now that you have input data, let’s create a stream that produces orders enriched with data from the customers and items tables.
The first thing to do is set the following property to ensure that you’re reading from the beginning of the stream:
SET 'auto.offset.reset' = 'earliest';
Creating the multi-way joined stream uses common SQL join syntax.
You define the fields you want to materialize in the stream with the SELECT
keyword, followed by source.field
identifiers. The FROM
keyword identifies the stream to base events off of and the JOIN
keywords identifies the joined tables and field relationships.
Joining "N" sources is equivalent to performing "N" joins consecutively, and the order of the joins is controlled by the order in which the joins are written. The multi-way join is subject to limitations and restrictions of each regular intermediate step join. See the ksqlDB documentation for the full details on joins.
CREATE STREAM orders_enriched AS
SELECT customers.customerid AS customerid, customers.customername AS customername,
orders.orderid, orders.purchasedate,
items.itemid, items.itemname
FROM orders
LEFT JOIN customers on orders.customerid = customers.customerid
LEFT JOIN items on orders.itemid = items.itemid;
This should yield the following output:
Message
----------------------------------------------
Created query with ID CSAS_ORDERS_ENRICHED_0
----------------------------------------------
Let’s view the result by selecting the values from our new enriched orders stream:
SELECT * FROM ORDERS_ENRICHED EMIT CHANGES LIMIT 6;
The output should look similar to:
+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
|ITEMS_ITEMID |CUSTOMERID |CUSTOMERNAME |ORDERID |PURCHASEDATE |ITEMNAME |
+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
|101 |1 |Adrian Garcia |abc123 |2020-05-01 |Television 60-in |
|102 |1 |Adrian Garcia |abc345 |2020-05-01 |Laptop 15-in |
|101 |2 |Robert Miller |abc678 |2020-05-01 |Television 60-in |
|101 |3 |Brian Smith |abc987 |2020-05-03 |Television 60-in |
|103 |2 |Robert Miller |xyz123 |2020-05-03 |Speakers |
|102 |2 |Robert Miller |xyz987 |2020-05-05 |Laptop 15-in |
Limit Reached
Query terminated
Finally, let’s see what’s available on the underlying Kafka topic for the new stream. We can print that out easily.
PRINT ORDERS_ENRICHED FROM BEGINNING LIMIT 6;
Key format: JSON or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2020/12/08 21:05:41.271 Z, key: 101, value: {"CUSTOMERID":"1","CUSTOMERNAME":"Adrian Garcia","ORDERID":"abc123","PURCHASEDATE":"2020-05-01","ITEMNAME":"Television 60-in"}, partition: 0
rowtime: 2020/12/08 21:05:41.300 Z, key: 102, value: {"CUSTOMERID":"1","CUSTOMERNAME":"Adrian Garcia","ORDERID":"abc345","PURCHASEDATE":"2020-05-01","ITEMNAME":"Laptop 15-in"}, partition: 0
rowtime: 2020/12/08 21:05:41.329 Z, key: 101, value: {"CUSTOMERID":"2","CUSTOMERNAME":"Robert Miller","ORDERID":"abc678","PURCHASEDATE":"2020-05-01","ITEMNAME":"Television 60-in"}, partition: 0
rowtime: 2020/12/08 21:05:41.357 Z, key: 101, value: {"CUSTOMERID":"3","CUSTOMERNAME":"Brian Smith","ORDERID":"abc987","PURCHASEDATE":"2020-05-03","ITEMNAME":"Television 60-in"}, partition: 0
rowtime: 2020/12/08 21:05:41.386 Z, key: 103, value: {"CUSTOMERID":"2","CUSTOMERNAME":"Robert Miller","ORDERID":"xyz123","PURCHASEDATE":"2020-05-03","ITEMNAME":"Speakers"}, partition: 0
rowtime: 2020/12/08 21:05:41.414 Z, key: 102, value: {"CUSTOMERID":"2","CUSTOMERNAME":"Robert Miller","ORDERID":"xyz987","PURCHASEDATE":"2020-05-05","ITEMNAME":"Laptop 15-in"}, partition: 0
Topic printing ceased
Notice that the key for each message is the Item ID of the order. This is the result of the join with the items table being the last join for our CREATE STREAM
command. The key of the last join will become the key of the records in the underlying topic.
Exit the ksqlDB CLI with the exit
command.
Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql
with the following content:
CREATE TABLE customers (customerid STRING PRIMARY KEY, customername STRING)
WITH (KAFKA_TOPIC='customers',
VALUE_FORMAT='json',
PARTITIONS=1);
CREATE TABLE items (itemid STRING PRIMARY KEY, itemname STRING)
WITH (KAFKA_TOPIC='items',
VALUE_FORMAT='json',
PARTITIONS=1);
CREATE STREAM orders (orderid STRING KEY, customerid STRING, itemid STRING, purchasedate STRING)
WITH (KAFKA_TOPIC='orders',
VALUE_FORMAT='json',
PARTITIONS=1);
CREATE STREAM orders_enriched AS
SELECT customers.customerid AS customerid, customers.customername AS customername,
orders.orderid, orders.purchasedate,
items.itemid, items.itemname
FROM orders
LEFT JOIN customers on orders.customerid = customers.customerid
LEFT JOIN items on orders.itemid = items.itemid;
Create a file at test/input.json
with the inputs for testing:
{
"inputs": [
{
"topic": "customers",
"key": "1",
"value": {
"customerid": "1",
"customername": "Adrian Garcia"
}
},
{
"topic": "customers",
"key": "2",
"value": {
"customerid": "2",
"customername": "Robert Miller"
}
},
{
"topic": "customers",
"key": "3",
"value": {
"customerid": "3",
"customername": "Brian Smith"
}
},
{
"topic": "items",
"key": "1",
"value": {
"itemid": "1",
"itemname": "Television 60-in"
}
},
{
"topic": "items",
"key": "2",
"value": {
"itemid": "2",
"itemname": "Laptop 15-in"
}
},
{
"topic": "items",
"key": "3",
"value": {
"itemid": "3",
"itemname": "Speakers"
}
},
{
"topic": "orders",
"key": "abc123",
"value": {
"orderid": "abc123",
"customerid": "1",
"itemid": "1",
"purchasedate": "2020-05-01"
}
},
{
"topic": "orders",
"key": "abc345",
"value": {
"orderid": "abc345",
"customerid": "1",
"itemid": "2",
"purchasedate": "2020-05-01"
}
},
{
"topic": "orders",
"key": "abc678",
"value": {
"orderid": "abc678",
"customerid": "2",
"itemid": "1",
"purchasedate": "2020-05-01"
}
},
{
"topic": "orders",
"key": "abc987",
"value": {
"orderid": "abc987",
"customerid": "3",
"itemid": "1",
"purchasedate": "2020-05-03"
}
},
{
"topic": "orders",
"key": "xyz123",
"value": {
"orderid": "xyz123",
"customerid": "2",
"itemid": "3",
"purchasedate": "2020-05-03"
}
},
{
"topic": "orders",
"key": "xyz987",
"value": {
"orderid": "xyz987",
"customerid": "2",
"itemid": "2",
"purchasedate": "2020-05-05"
}
}
]
}
Similarly, create a file at test/output.json
with the expected outputs.
{
"outputs": [
{
"topic": "ORDERS_ENRICHED",
"key": "1",
"value": {
"CUSTOMERID": "1",
"CUSTOMERNAME": "Adrian Garcia",
"ORDERID": "abc123",
"PURCHASEDATE": "2020-05-01",
"ITEMNAME": "Television 60-in"
}
},
{
"topic": "ORDERS_ENRICHED",
"key": "2",
"value": {
"CUSTOMERID": "1",
"CUSTOMERNAME": "Adrian Garcia",
"ORDERID": "abc345",
"PURCHASEDATE": "2020-05-01",
"ITEMNAME": "Laptop 15-in"
}
},
{
"topic": "ORDERS_ENRICHED",
"key": "1",
"value": {
"CUSTOMERID": "2",
"CUSTOMERNAME": "Robert Miller",
"ORDERID": "abc678",
"PURCHASEDATE": "2020-05-01",
"ITEMNAME": "Television 60-in"
}
},
{
"topic": "ORDERS_ENRICHED",
"key": "1",
"value": {
"CUSTOMERID": "3",
"CUSTOMERNAME": "Brian Smith",
"ORDERID": "abc987",
"PURCHASEDATE": "2020-05-03",
"ITEMNAME": "Television 60-in"
}
},
{
"topic": "ORDERS_ENRICHED",
"key": "3",
"value": {
"CUSTOMERID": "2",
"CUSTOMERNAME": "Robert Miller",
"ORDERID": "xyz123",
"PURCHASEDATE": "2020-05-03",
"ITEMNAME": "Speakers"
}
},
{
"topic": "ORDERS_ENRICHED",
"key": "2",
"value": {
"CUSTOMERID": "2",
"CUSTOMERNAME": "Robert Miller",
"ORDERID": "xyz987",
"PURCHASEDATE": "2020-05-05",
"ITEMNAME": "Laptop 15-in"
}
}
]
}
Lastly, invoke the tests using the test runner and the statements file that you created earlier:
docker exec ksqldb-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json
Which should pass:
>>> Test passed!
Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.
Sign up for Confluent Cloud, a fully managed Apache Kafka service.
After you log in to Confluent Cloud Console, click Environments
in the lefthand navigation, click on Add cloud environment
, and name the environment learn-kafka
. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.
From the Billing & payment
section in the menu, apply the promo code CC100KTS
to receive an additional $100 free usage on Confluent Cloud (details).
Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.
Next, from the Confluent Cloud Console, click on Clients
to get the cluster-specific configurations, e.g., Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.
Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.